PitVis-2023 challenge: Workflow recognition in videos of endoscopic pituitary surgery.

The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery, including: which surgical steps are performed; and which surgical instruments are used. This information can later be used to assist clinicians when learning the surgery or during live surgery.

Mapping patient encounters to identify recruitment timepoints after brain tumour surgery: a cohort and cross-sectional study.

Objective: This study aims to develop a comprehensive process map for patients with brain tumours to identify opportunities for quality improvement and automated data collection. Through optimising workflows, the overall goal is to improve patient recruitment to clinical trials.

Design: A two-stage mixed methods design, combining qualitative development of a process map with quantitative validation using electronic health records (EHR).

Concept Recognition and Characterization of Patients Undergoing Resection of Vestibular Schwannoma Using Natural Language Processing.

 Natural language processing (NLP), a subset of artificial intelligence (AI), aims to decipher unstructured human language. This study showcases NLP's application in surgical health care, focusing on vestibular schwannoma (VS). By employing an NLP platform, we identify prevalent text concepts in VS patients' electronic health care records (EHRs), creating concept panels covering symptomatology, comorbidities, and management.

Automated Operative Phase and Step Recognition in Vestibular Schwannoma Surgery: Development and Preclinical Evaluation of a Deep Learning Neural Network (IDEAL Stage 0).

Background And Objectives: Machine learning (ML) in surgical video analysis offers promising prospects for training and decision support in surgery. The past decade has seen key advances in ML-based operative workflow analysis, though existing applications mostly feature shorter surgeries (<2 hours) with limited scene changes. The aim of this study was to develop and evaluate a ML model capable of automated operative workflow recognition for retrosigmoid vestibular schwannoma (VS) resection.

ConsisTNet: a spatio-temporal approach for consistent anatomical localization in endoscopic pituitary surgery.

Purpose: Automated localization of critical anatomical structures in endoscopic pituitary surgery is crucial for enhancing patient safety and surgical outcomes. While deep learning models have shown promise in this task, their predictions often suffer from frame-to-frame inconsistency. This study addresses this issue by proposing ConsisTNet, a novel spatio-temporal model designed to improve prediction stability.

Applicability of a Dexterity-Enhancing Handheld Robot for 360° Endoscopic Skull Base Approaches: An Exploratory Cadaver Study.

Background And Objectives: Endoscopic skull base surgery aims to reduce surgical morbidity by minimizing tissue manipulation and exposure. However, the anatomic constraints posed by the narrow surgical corridors and constrained operative workspace present technical challenges due to reduced dexterity. This study evaluates the applicability of a novel dexterity-enhancing handheld robot for endoscopic skull base approaches.

Evaluating the impact of polypharmacy in elderly patients undergoing neurosurgery: a retrospective study.

Objectives: This study aimed to evaluate the impact of polypharmacy on length of hospital stay (LOS) and occurrence of postoperative complications in elderly patients undergoing neurosurgery.

Methods: This study represents a single-centre retrospective review of consecutive patients above 65 years old undergoing elective or emergency neurosurgery between June 2021 and January 2022. Patients were categorised into two groups based on the presence (five or more medications) or absence of polypharmacy.

A Handheld Robot for Endoscopic Endonasal Skull Base Surgery: Updated Preclinical Validation Study (IDEAL Stage 0).

 Endoscopic endonasal surgery (EES) has become increasingly popular, yet anatomical constraints posed by the nose and limitations of nonarticulated instruments render EES technically challenging, with a steep associated learning curve. Therefore, we developed a handheld robot to enhance dexterity in endoscopic neurosurgical procedures. A previous trial of the robot demonstrated its potential advantages in endoscopic neurosurgery but also the need for improvements.

Diagnosis of subarachnoid haemorrhage: Systematic evaluation of CT head diagnostic accuracy and comparison with the 2022 NICE guidelines.

Introduction: Aneurysmal subarachnoid haemorrhage has a high incidence, and morbidity. It has been suggested that a negative non-contrast CT head can rule out SAH if performed within 6 h of symptom onset.

Research Question: What is the sensitivity of CT head at ruling out SAH stratified by time-point, and what is the potential impact of omitting Lumbar Puncture (LP) from the diagnostic pathway?

Material And Methods: Systematic review and meta-analysis (PROSPEROID CRD42022379929).

Nanoscale Viscometry Reveals an Inherent Mucus Defect in Cystic Fibrosis.

The abnormally viscous and thick mucus is a hallmark of cystic fibrosis (CF). How the mutated CF gene causes abnormal mucus remains an unanswered question of paramount interest. Mucus is produced by the hydration of gel-forming mucin macromolecules that are stored in intracellular granules prior to release.

Automated surgical skill assessment in endoscopic pituitary surgery using real-time instrument tracking on a high-fidelity bench-top phantom.

Improved surgical skill is generally associated with improved patient outcomes, although assessment is subjective, labour intensive, and requires domain-specific expertise. Automated data-driven metrics can alleviate these difficulties, as demonstrated by existing machine learning instrument tracking models. However, these models are tested on limited datasets of laparoscopic surgery, with a focus on isolated tasks and robotic surgery.

PitRSDNet: Predicting intra-operative remaining surgery duration in endoscopic pituitary surgery.

Accurate intra-operative Remaining Surgery Duration (RSD) predictions allow for anaesthetists to more accurately decide when to administer anaesthetic agents and drugs, as well as to notify hospital staff to send in the next patient. Therefore, RSD plays an important role in improved patient care and minimising surgical theatre costs via efficient scheduling. In endoscopic pituitary surgery, it is uniquely challenging due to variable workflow sequences with a selection of optional steps contributing to high variability in surgery duration.

Bitter taste receptor T2R14-Gαi coupling mediates innate immune responses to microbial quorum sensing molecules in cystic fibrosis.

Cystic fibrosis (CF) is an autosomal recessive disease characterized by microbial infection and progressive decline in lung function, leading to significant morbidity and mortality. The bitter taste receptor T2R14 is a chemosensory receptor that is significantly expressed in airways. Using a combination of cell-based assays and T2R14 knockdown in bronchial epithelial cells from CF and non-CF individuals, we observed that T2R14 plays a crucial role in the detection of bacterial and fungal signals and enhances host innate immune responses.

Artificial intelligence assisted operative anatomy recognition in endoscopic pituitary surgery.

Pituitary tumours are surrounded by critical neurovascular structures and identification of these intra-operatively can be challenging. We have previously developed an AI model capable of sellar anatomy segmentation. This study aims to apply this model, and explore the impact of AI-assistance on clinician anatomy recognition.

Artificial Intelligence Assisted Surgical Scene Recognition: A Comparative Study Amongst Healthcare Professionals.

Objective: This study aimed to compare the ability of a deep-learning platform (the MACSSwin-T model) with healthcare professionals in detecting cerebral aneurysms from operative videos. Secondly, we aimed to compare the neurosurgical team's ability to detect cerebral aneurysms with and without AI-assistance.

Background: Modern microscopic surgery enables the capture of operative video data on an unforeseen scale.

Video-Based Performance Analysis in Pituitary Surgery-Part 1: Surgical Outcomes.

Background: Endoscopic pituitary adenoma surgery has a steep learning curve, with varying surgical techniques and outcomes across centers. In other surgeries, superior performance is linked with superior surgical outcomes. This study aimed to explore the prediction of patient-specific outcomes using surgical video analysis in pituitary surgery.

Mapping the Clinical Pathway for Patients Undergoing Vestibular Schwannoma Resection.

Background: The introduction of the electronic health record (EHR) has improved the collection and storage of patient information, enhancing clinical communication and academic research. However, EHRs are limited by data quality and the time-consuming task of manual data extraction. This study aimed to use process mapping to help identify critical data entry points within the clinical pathway for patients with vestibular schwannoma (VS) ideal for structured data entry and automated data collection to improve patient care and research.

Empowering Early Career Neurosurgeons in the Critical Appraisal of Artificial Intelligence and Machine Learning: The Design and Evaluation of a Pilot Course.

Background: Artificial intelligence (AI) is expected to play a greater role in neurosurgery. There is a need for neurosurgeons capable of critically appraising AI literature to evaluate its implementation or communicate information to patients. However, there are a lack of courses delivered at a level appropriate for individuals to develop such skills.

Mapping patient education encounters in elective surgery: a cohort study and cross-sectional survey.

Objective: Develop a process map of when patients learn about their proposed surgery and what resources patients use to educate themselves.

Design: A mixed methods design, combining semistructured stakeholder interviews, quantitative validation using electronic healthcare records (EHR) in a retrospective cohort and a cross-sectional patient survey.

Setting: A single surgical centre in the UK.

PitSurgRT: real-time localization of critical anatomical structures in endoscopic pituitary surgery.

Purpose: Endoscopic pituitary surgery entails navigating through the nasal cavity and sphenoid sinus to access the sella using an endoscope. This procedure is intricate due to the proximity of crucial anatomical structures (e.g.

A randomised, double blind, placebo-controlled trial of a two-week course of dexamethasone for adult patients with a symptomatic Chronic Subdural Haematoma (Dex-CSDH trial).

Background: Chronic subdural haematoma is a collection of 'old blood' and its breakdown products in the subdural space and predominantly affects older people. Surgical evacuation remains the mainstay in the management of symptomatic cases.

Objective: The Dex-CSDH (DEXamethasone in Chronic SubDural Haematoma) randomised trial investigated the clinical effectiveness and cost-effectiveness of dexamethasone in patients with a symptomatic chronic subdural haematoma.

mRNA Delivery: Challenges and Advances through Polymeric Soft Nanoparticles.

Single-stranded messenger ribonucleic acid (mRNA) plays a pivotal role in transferring genetic information, and tremendous effort has been devoted over the years to utilize its transcription efficacy in therapeutic interventions for a variety of diseases with high morbidity and mortality. Lipid nanocarriers have been extensively investigated for mRNA delivery and enabled the rapid and successful development of mRNA vaccines against SARS-CoV-2. Some constraints of lipid nanocarriers have encouraged the development of alternative delivery systems, such as polymer-based soft nanoparticles, which offer a modular gene delivery platform.

Can ChatGPT outperform a neurosurgical trainee? A prospective comparative study.

Purpose: This study aimed to compare the performance of ChatGPT, a large language model (LLM), with human neurosurgical applicants in a neurosurgical national selection interview, to assess the potential of artificial intelligence (AI) and LLMs in healthcare and provide insights into their integration into the field.

Methods: In a prospective comparative study, a set of neurosurgical national selection-style interview questions were asked to eight human participants and ChatGPT in an online interview. All participants were doctors currently practicing in the UK who had applied for a neurosurgical National Training Number.